SCI Publications
2009
J.R. Anderson, B.W. Jones, J.-H. Yang, M.V. Shaw, C.B. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R.E. Marc.
“A Computational Framework for Ultrastructural Mapping of Neural Circuitry,” In PLoS Biology, Vol. 7, No. 3, pp. e74. 2009.
PubMed ID: 19855814
J. Anderson, B. Jones, J. Yang, M. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, Kannan U.V., R.T. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc.
“Ultra Structural Mapping of Neural Circuitry: A Computational Framework,” In IEEE International Symposium on Biomedical Engineering (ISBI 2009), pp. 1135--1137. 2009.
DOI: 10.1109/ISBI.2009.5193257
Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and subsequently register distorted mosaics. Moreover, most cell or tissue class markers are not optimized for TEM imaging. We present a complete framework for neuronal reconstruction at ultrastructural resolution, allowing the elucidation of complete neuronal circuits. This workflow combines TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration and terabyte-scale image browsing for volume annotation. Networks that previously would require decades of assembly can now be completed in months, enabling large-scale connectivity analyses of both new and legacy data. Additionally, these approaches can be extended to other tissue or biological network systems.
Keywords: crcns, neural circuitry
S. Gerber, T. Tasdizen, S. Joshi, R.T. Whitaker.
“On the Manifold Structure of the Space of Brain Images,” In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2009), Springer, pp. 305--312. 2009.
DOI: 10.1007/978-3-642-04268-3_38
PubMed ID: 20426001
S. Gerber, T. Tasdizen, R.T. Whitaker.
“Dimensionality Reduction and Principal Surfaces via Kernel Map Manifolds,” In Proceedings of the 2009 International Conference on Computer Vison (ICCV 2009), pp. 529--536. September, 2009.
ISSN: 1550-5499
DOI: 10.1109/ICCV.2009.5459193
We present a manifold learning approach to dimensionality reduction that explicitly models the manifold as a mapping from low to high dimensional space. The manifold is represented as a parametrized surface represented by a set of parameters that are defined on the input samples. The representation also provides a natural mapping from high to low dimensional space, and a concatenation of these two mappings induces a projection operator onto the manifold. The explicit projection operator allows for a clearly defined objective function in terms of projection distance and reconstruction error. A formulation of the mappings in terms of kernel regression permits a direct optimization of the objective function and the extremal points converge to principal surfaces as the number of data to learn from increases. Principal surfaces have the desirable property that they, informally speaking, pass through the middle of a distribution. We provide a proof on the convergence to principal surfaces and illustrate the effectiveness of the proposed approach on synthetic and real data sets.
E. Jurrus, M. Hardy, T. Tasdizen, P.T. Fletcher, P. Koshevoy, C.-B. Chien, W. Denk, R.T. Whitaker.
“Axon Tracking in Serial Block-Face Scanning Electron Microscopy,” In Medical Image Analysis (MEDIA), Vol. 13, No. 1, Elsevier, pp. 180--188. February, 2009.
PubMed ID: 18617436
E. Jurrus, A.R.C. Paiva, S. Watanabe, R.T. Whitaker, E.M. Jorgensen, T. Tasdizen.
“Serial Neural Network Classifier for Membrane Detection using a Filter Bank.,” SCI Technical Report, No. UUSCI-2009-006, SCI Institute, University of Utah, 2009.
E. Jurrus, A.R.C. Paiva, S. Watanabe, R. Whitaker, E.M. Jorgensen, T. Tasdizen.
“Serial Neural Network Classifier for Membrane Detection using a Filter Bank,” In Proc. Workshop on Microscopic Image Analysis with Applications in Biology, Bethesda, MD, USA, 2009.
J.S. Preston, T. Tasdizen, C.M. Terry, A.K. Cheung, R.M. Kirby.
“Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability,” In IEEE Transactions on Biomedical Engineering, Vol. 56, No. 3, Note: Epub 2008 Dec 2, pp. 609--620. 2009.
PubMed ID: 19272865
T. Tasdizen.
“Principal neighborhood dictionaries for nonlocal means image denoising,” In IEEE Transactions on Image Processing, Vol. 18, No. 12, Note: Epub 2009 Jul 24, pp. 2649--2660. 2009.
PubMed ID: 19635697
Kannan U.V., A.R.C. Paiva, E. Jurrus, T. Tasdizen.
“Automatic Markup of Neural Cell Membranes Using Boosted Decision Stumps,” In Proceedings of the IEEE International Symposium on Biomedical Engineering (ISBI 2009), Boston, MA, pp. 1039--1042. 2009.
DOI: 10.1109/ISBI.2009.5193233
To better understand the central nervous system, neurobiologists need to reconstruct the underlying neural circuitry from electron microscopy images. One of the necessary tasks is to segment the individual neurons. For this purpose, we propose a supervised learning approach to detect the cell membranes. The classifier was trained using AdaBoost, on local and context features. The features were selected to highlight the line characteristics of cell membranes. It is shown that using features from context positions allows for more information to be utilized in the classification. Together with the nonlinear discrimination ability of the AdaBoost classifier, this results in clearly noticeable improvements over previously used methods.
Keywords: crcns, neural networks
2008
N.L. Foster, A.Y. Wang, T. Tasdizen, P.T. Fletcher, J.M. Hoffman, R.A. Koeppe.
“Realizing the Potential of Positron Emission Tomography with 18F-Fluorodeoxyglucose to Improve the Treatment of Alzheimer,” In Journal of the Alzheimer, Vol. 4, No. 1, Suppl. 1, pp. S29--36. 2008.
PubMed ID: 18631997
N. L. Foster, A.Y. Wang, T. Tasdizen, K. Chen, W. Jagust, R.A. Koeppe, E. Reiman, M.W. Weiner, S. Minoshima.
“Cerebral Hypometabolism Suggesting Frototemporal Dementia in an Alzheimer’s Disease Clinical Trial,” In Neurology, Vol. 70, No. 11, pp. A103. 2008.
E. Jurrus, R.T. Whitaker, B. Jones, R. Marc, T. Tasdizen.
“An Optimal-Path Approach for Neural Circuit Reconstruction,” In Proceedings of the 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1609--1612. 2008.
PubMed ID: 19172170
N. Sadeghi, N.L. Foster, A.Y. Wang, S. Minoshima, A.P. Lieberman, T. Tasdizen.
“Automatic Classification of Alzheimer,” In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI 2008): From Nano to Macro, pp. 408--411. 2008.
DOI: 10.1109/ISBI.2008.4541019
T. Tasdizen.
“Principal Components for Non-Local Means of Image Denoising,” In Proceedings of the International Conference on Image Processing (ICIP 2008), pp. 1728--1731. 2008.
PubMed ID: 19180227
T. Tasdizen , E. Jurrus, R. T. Whitaker.
“Non-uniform Illumination Correction in Transmission Electron Microscopy,” In MICCAI Workshop on Microscopic Image Analysis with Applications in Biology, 2008.
Transmission electron microscopy (TEM) provides resolutions on the order of a nanometer. Hence, it is a critical imaging modality for biomedical analysis at the sub-cellular level. One of the problems associated with TEM images is variations in brightness due to electron imaging defects or non-uniform support films and specimen staining. These variations render image processing operations such as segmentation more difficult. The correction requires estimation of the global illumination field. In this paper, we propose an automatic method for estimating the illumination field using only image intensity gradients. The closed-form solution is very fast to compute.
2007
G. Adluru, S.P. Awate, T. Tasdizen, R.T. Whitaker, E.V.R. DiBella.
“Temporally Constrained Reconstruction of Dynamic Cardiac Perfusion MRI,” In Magnetic Resonance in Medicine, Vol. 57, pp. 1027--1036. 2007.
S. Gerber, T. Tasdizen, R.T. Whitaker.
“Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps,” In Proceedings of the 2007 International Conference on Machine Learning (ICML), pp. 281--288. 2007.
B.W. Jones, R.E. Marc, C.B. Watt, K. Kinardi, D. DeMill, J.H. Yang, T. Tasdizen, P. Koshevoy, E. Jurrus, R.T. Whitaker.
“Structure and Function of Microneuromas in Retinal Remodeling,” In The Association for Research in Vision and Ophthalmology (ARVO) Conference, Note: (abstract), 2007.
P.A. Koshevoy, T. Tasdizen, R.T. Whitaker.
“Automatic Assembly of TEM Mosaics and Mosaic Stacks Using Phase Correlation,” SCI Institute Technical Report, No. UUSCI-2007-004, University of Utah, 2007.